Watershed Scale Water Quality Impacts Of Cover Crop Adaptation In A Two-stage Ditch System - Dr. Brittany Hanrahan, from the 2018 Conservation Tillage and Technology Conference, March 6 - 7, Ada, OH, USA.
More presentations at https://www.youtube.com/channel/UCZBwPfKdlk4SB63zZy16kyA
2. Agricultural land use and the export of excess nutrients
results in algal blooms followed by hypoxia
Excess Nutrients
Algal Blooms
Algal Death & Decomposition
Water Column Hypoxia
Lake Erie (SRP)
Gulf of Mexico (NO3
-)
Peak run-off often
occurs during
spring snowmelt
and storms.Boat wake, Maumee Bay, August 2015
Western Lake Erie algal bloom
July 2015 (NASA)
NOAA
3. Streams draining row crop agriculture export excess
nutrients and sediments
• For example, in Indiana, >90% of the ~50,000 km of stream/ditches are
located within 500m of a row-crop field; land cover >80% row-crop.
• Channelization, tile drainage, and fertilizer addition improve crop yields,
but these practices also reduce nutrient retention and channel stability.
Net Result: increased export of excess nutrients and sediments to
downstream water bodies.
4. Watershed Boundary
Shatto Ditch
Stream Sampling Site
Tile Drain Location
NH4
+ = 143 ug N/L
SRP = 106 ug P/L
NO3
- = 5.4 mg N/L
• Agricultural watershed located
in Kosciusko County, IN
• Tributary of the Tippecanoe R.
• Watershed characteristics:
• Total area: 1333 ha
• Agricultural area: ~80%
dominated by corn & soy
• Stream Characteristics:
• High nutrient concentrations
year-round
• Presentation Outline:
• In-stream: two-stage ditch
• Watershed: cover crops
Shatto Ditch Watershed
Project Context: Study Site
Average flow = ~116 L/s;
Range = <10 – 2000 L/s
5. 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Two-Stage Ditch Implementation
↑ bank stability
↑ denitrification
↑ water quality
Only decreased total
NO3
--N export by ~10%
Landscape
Context
How does the
two-stage
function ~10
years later?
Project Context: Timeline
6. In-stream restoration:
Two-stage ditch
• Engineered for channel stability
• Increased sedimentation →
particles settle out on floodplains
• Increased nutrient retention→ more
time/space for removal
Channelized
During late-fall construction
After one growing season
Floodplains each 3-4 m wide
Stream width triples during storms
7. Does the two stage improve water quality?
• Two-stage reduced water
column turbidity, even as
practice “aged”.
• Mechanism: two-stage slows
water velocities during storms,
promotes sediment and P
retention on floodplains.
• During floodplain inundation,
two-stage increased
denitrification N-removal (3-
24x higher); also increased as
two-stage “matures” over time.
• Mechanism: two-stage ditch
enhances nutrient retention on
floodplains by tripling
bioreactive surface area.
Turbidity during floodplain inundation
(1 yr) (2 yr) (3 yr) (5 yr) (7 yr) (Nat.)
Increasing two-stage age (years)
CRE SHA KLA CRO BUL CEAP
Turbidity(NTU)
0
100
200
300
400
500
*
* * *
*
Channelized
Two-stage
N-removal during
inundation
(1 yr) (2 yr) (3 yr) (5 yr) (7 yr) (Nat.)
CRE SHA KLA CRO BUL CEP
Reach-scaleN-removal
(kgN/km/d)
0
4
8
12
16
23x
3x
4x
24x
4x
7x
Stream channel
Floodplain
Davis et al. 2015, Mahl et al. 2015, JAWRA.
8. The two-stage is a viable tool in the “nutrient
management toolbox” and can be implemented to
improve water quality while maintaining productive
agriculture.
Best practices to maximize improvements with two-
stage occur when floodplain benches:
• inundate regularly (i.e., >12 events/yr).
• retain tile outflows for as long as possible.
• are vegetated and allowed to “age” over time.
9. SDW: 10 years later
Roley et al. Eco Apps 2012
Two-Stage DitchChannelized
Two-stage: After 10 yearsChannelized: After 10 years
• In 2007, a two-stage ditch was
constructed in 600 m of
Shatto Ditch Watershed
• Established treatment and
control reaches for comparison
No maintenance
or dredging has
occurred since
2007
10. Reference Two-Stage
DN(ugN[gDM]-1hr-1)
0.0
0.1
0.2
0.3
0.4
Reference Two-Stage
ER(ugO2[gDM]-1hr-1) 0.0
0.5
1.0
1.5
2.0
How does the two-stage in the Shatto Ditch
function after 10-years?
*
• DN was >30% higher in the
restored, two-stage than in the
naturalizing reference
2-way ANOVA
Reach: p<0.05
Floodplain Soil Organic Matter (%)
0 5 10 15 20
DN(ugN[gDM]
-1
hr-1)
0.00
0.25
0.50
0.75
Naturalized Restored
OrganicMatter(%)
0
2
4
6
8
10
12
Res
.
%OM
*
• DN ↑ with %OM in floodplain soils;
driven by higher OM in restored
(+22%)
SLR; r2=0.56, p<0.001
Nat.
Hanrahan et al. Biogeochemistry 2018
Immediately after
construction
After 1 growing season
Vegetation re-established rapidly
↑ OM via growth/decomp
Channelized
11. Two-stage jump starts ecosystem function
• Area of two-stage floodplain = 6x higher than “naturalizing” channelized
• >30 years until equal area of the two-stage
• No loss of drainage capacity in the two-stage system
Both systems effectively remove N, but two-stage implementation restores
DN rapidly and requires little to no maintenance.
12. 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Two-Stage Ditch Implementation
↑ bank stability
↑ denitrification
↑ water quality
Only decreased total
NO3
--N export by ~10%
Watershed-scale planting of winter
cover crops
Landscape
Context
Project Context: Timeline
13. Harvest Begins
and Cover
Crops Planted
Cover Crops
Overwinters
Cover Crops grow
during critical
times of N and P
export
• Crops that are planted after cash-crop harvest and grow when fields are
normally fallow or bare
• Field-scale studies: cover crops ↓N export via tile drains by 13-94%
• However, the goal of our study examine the influence of planting cover crops at the
watershed-scale on nutrient loss from “working lands”
May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr
Cover crops have the potential to address
nutrient loss from agricultural watersheds
14. SHATTO DITCH WATERSHED USDA-CIG (2012-2015)
• Oct 2012-13: pre-treatment, 320 acres (12% of croppable acres) in cover crops
• Oct 2013-14: cover crops↑ to 1,610 (67%); note: Indiana avg = 5%
• Oct 2014-15: cover crops on 1,561 acres; projected ↑ to 1,660 in 2015-16
• Planted mostly annual ryegrass (“farmer choice”), no other stipulations
GOAL: “saturate” watershed with cover crops, isolate effect of watershed-scale
cover crops, measure dissolved N and P loss.
3,300 acres,
85% row crop
16. • Sampling Sites:
• Identified tile drain outlets along 8 km
of stream from “headwaters” to outlet
at the Tippecanoe River
• Sampling Frequency:
• Collect water samples every 14 days:
• 23 tile drain outlets (~2 per field)
• Watershed oultlet
• Samples analyzed for NO3
--N and SRP
on Lachat Flow Injection Auto Analyzer
• Tile load calculations:
• Measure instantaneous Q (L s-1)
at each tile drain
Watershed outlet
Tile drain outlet
Flow to
Tippecanoe
River
Methods: Field and Lab
17. Sampling Date
Oct-12 Apr-13 Oct-13 Apr-14 Oct-14 Apr-15 Oct-15 Apr-16 Oct-16
Average[NO3
-
-N](mgL
-1
)
0
5
10
15
20
25
Stream
Bare Soil - No Cover Crops
Cover Crops
TEMPORAL PATTERNS OF STREAM AND TILE DRAIN [NITRATE]
• 2013: tile drain NO3
- without cover crops higher than with cover crops,
especially during Winter/Spring.
• 2014 - 2016: tile NO3
- in Year 1-3 of cover crop planting is lower; similar or
even lower than tiles with cover crops for >2 years.
• Approach: stream and tile drain samples every 14d to
quantify the impact of cover crops on water quality.
Hanrahan and Tank, unpublished data.
Pre-Treatment
2012-2013
Year 1
2013-2014
Year 2
2014-2015
Stream
Bare Soil - No Cover Crops
Cover Crops
Year 3
2015-2016
19. ESTIMATE NUTRIENT MASS LOSS VIA TILE DRAINS
NUTRIENT CONCENTRATION (MG/L) * FLOW (L/S) = FLUX (KG/DAY)
20. Data with outliers removed
Spring 2013Summer 2013 1 2 3 4 5 6 7 8 9 10 11 12
Average[NO3
-]Flux(kgN/day)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Tile
Tile
Tile
COVER CROPS REDUCE NITRATE EXPORT FROM FIELDS ESPECIALLY DURING SPRING
TILE DRAIN NITRATE FLUX
Hanrahan and Tank, unpublished data.
Spr Sum Fall Win
Pre-Treatment
2012-2013
Year 1
2013-2014
Year 2
2014-2015
↓ 55%
in Spring
2014
↓ 47%
in Spring
2015
Year 3
2015-2016
Spr Sum Fall Win Spr Sum Fall Win Spr Sum
↓ 83%
in Spring
2016
Tile Drain No Cover Crops 2013
Tile Drain Cover Crops 2013
Tile Drain Cover Crops 2014-2016
21. Spring 2013Summer 2013 1 2 3 4 5 6 7 8 9 10 11 12
AverageSRPFlux(g/day)
0
2
4
6
8
10
12
14
Tile D
Tile D
Tile D
COVER CROPS REDUCE SRP EXPORT FROM FIELDS, BOTH DURING SPRING & SUMMER
Hanrahan and Tank, unpublished data.
Pre-Treatment
2012-2013
Year 1
2013-2014
Year 2
2014-2015
TILE DRAIN SRP FLUX
↓ 50%
in Spring
2014
↓ 78%
in Spring
2015
Spr Sum Fall Win Spr Sum Fall Win Spr Sum Fall Win Spr Sum
Year 3
2015-2016
Tile Drain No Cover Crops 2013
Tile Drain Cover Crops 2013
Tile Drain Cover Crops 2014-2016
↓ 82%
in Spring
2016
22. ESTIMATING WATERSHED-SCALE NUTRIENT EXPORT
• Use stage-discharge relationship to estimate daily Q.
• Apply C-Q relationship to estimate daily [NO3] or [SRP]
• Daily Q * [N or P] = N or P per day exported from SDW annual.
67-70% cover crops12% cover crops
24. Results Slide 1
Does cover crop planting reduce N and P
export from the watershed outlet?
Annual
NO3
-
Yield(kgha
-1
)
0
5
10
15
20
25
30
Water Year
2008 2009 2010 2011 2012 2013 2014 2015 2016
Annual
SRPYield(kgha
-1
)
0.0
0.1
0.2
0.3
0.4
0.5
Discharge(Ls-1)
0
1000
2000
3000
Discharge
Mean = 19 kg NO3
- ha-1 17 kg NO3
- ha-1
Mean = 0.24 kg SRP ha-1
0.17 kg SRP ha-1
• Average of total annual
NO3
- export in post cover
crop period = ↓11%
• Average of total annual
SRP export in post cover
crop period = ↓30%
• Monthly and seasonal
totals = also ↓ export in
post cover crop period
• Need to account for
variability in flow
Pre-Cover Crops
(2008-2013)
Post Cover Crops
(2014-2016)
25. Results Slide 1
Cover crops reduced N and P loss from the
Shatto Ditch Watershed
• ↓ [NO3
--N] and [SRP] from tiles
with cover crops
• ↓ NO3
--N and SRP flux or mass
loss from tiles with cover crops
• ↓ average watershed NO3
--N
and SRP export during post-
cover crop years
• Challenging to detect
statistically
• Cover crops may help address
N and P loss from agricultural
watersheds
26. Acknowledgements
Advisor
Dr. Jennifer L. Tank
Tank Labbers
Dr. Sheila Christopher, Arial Shogren, Martha
Dee, Matt Trentman, Shannon Speir, Ursula
Mahl, Eric Pitts, Rob Davis, Sarah Winikoff;
Drs. Sarah Roley, AJ Reisinger, Peter Levi,
Laura Johnson, Natalie Griffiths, Tim
Hoellein, Denise Bruesewitz
Undergraduates
Elizabeth Berg, Anna Kottkamp, Karen
Huang, Mary Mecca, Kyle White; Nicole
Gorman, Eddie Lopez, Matt Kirian, Erik
Maag, Audrey Thellman
IWI/ECI
Lizzie Willows, Kara Prior, Lienne Sethna,
Brett Peters, Milan Budhathoki, Kemal
Gokkaya, Peter Annin, Alex Gumm, Joanna
McNulty,
CEST
Suzyanne Guzicki,
Jon Loftus
Collaborators
Dr. Antoine Aubeneau, Dr. Diogo Bolster, Dr.
Emma Rosi, Dr. Jen Drummond, Dr. Sara
McMillan, Dr. Robert Hall; Dr. Kevin King, Dr.
Mark Williams, Dr. Chi-hua Huang, Stan
Livingston; Kent Wamsley; Andrea Baker,
Chad Schotter, Darci Zolman, SWCD Board
Landowners
Robert Foltz, Michael Long, The Romine
Family, The Severns Family, Steven Miler and
Creighton Brothers LLC; Jaimie Scott
Agriculture is the dominant land use across much of the Mississippi River Basin; particularly in the Midwestern Corn Belt This figure show agricultural land use in light green
But excess nutrients that leave or runoff agricultural fields into adjacent streams can have negative consequences on downstream water bodies
Excess nitrogen and phosphorus are transported to downstream systems where they fuel, or fertilize algal blooms
When those algal blooms die and decompose, oxygen is stripped from the water column causing areas of little to no oxygen often leading to hypoxia (no oxygen) where aquatic organisms can no longer survive
In the Gulf of Mexico, excess NO3 creates a “dead zone” every year that ranges in size from Connecticut to New Jersey (nearly XXXX square miles)
Closer to home, algal blooms in Lake Erie are fueled by soluble or dissolved reactive phosphorus
Additonally, peak runoff often occurs during spring storm and snowmelt events so these algal blooms generally occur from the spring to summer (when people want to be out enjoying these water bodies!)
These issues are compounded by conventional management practices of agricultural systems like surface and subsurface drainage systems that are prevalent throughout the Midwest
For example, in Indiana where I conducted my dissertation research, >90% of the >48,000km of streams (~30,000 miles) are within 500 m (~1600 ft) of a row-crop field
Additionally, drainage systems (channelized streams) are designed to move water quickly and efficiently away from fields – making them effective transporters of nutrients that enter from adjacent fields
While these practices – channelization, tile drainage, and fertilizer addition – improve crop yields, they also reduce nutrient retention and channel stability in agricultural streams
With the net result being increased export of excess nutrients and sediments to downstream water bodies
The work I’m going to talk about today all took place in the Shatto Ditch Watershed
Which is a small, agricultural watershed located in Kosciusko County, IN
The ditch itself is a tributary of the Tippecanoe River, which is important because the Tippe has “high” freshwater mussel diversity and is listed as one of the Nature Conservancy’s “last great places”
The watersheds is a little over 1300 ha and nearly 80% of the land use is for row-crop agriculture in corn or soybeans
The stream itself is a relatively small, first-order stream with average flow of ~120L/s; but flow can range anywhere from <10L/s in the late summer/early fall to >2000L/s during spring storms
And nutrient concentrations are very high consistent with streams across much of the Midwest
So I’m going to split this talk into two parts today by first talking about the two-stage ditch and then talking about watershed-scale implementation of cover crops
First, I want to provide some context because this hasn’t always been a watershed-scale project. I’m going to use this timeline as a way to organize the “phases” of this project because it sort of grew organically
Work in the Shatto Ditch Watershed (located in Kosciusko County, IN) began as a demonstration project for the two-stage ditch
In 2007, 600 m of two-stage ditch was constructed at the bottom of the watershed. The effects of the two-stage were then measured/quantified extensively from ~2007-2012 and I’ll summarize some of these findings in the next few slides.
We then “returned” to the two-stage in 2016 to examine/measure it’s function 10 years after construction
I’ve already mentioned the channelized ditch typical of Midwestern agricultural systems and how these ditches are designed to move (or remove) water as quickly and efficiently as possible.
As such, these systems have extremely “flashy” hydrographs (respond rapidly to precipitation with large increases in flow (depth), and return to baseflow quickly) and also unstable – the speed with which this water moves creates incredible stress on the stream banks that causes bank erosion/slumping. This is BAD for farmers because it slowly encroaches on their land, resulting in a LOST of acreage.
Originally, the two-stage ditch was designed to increase bank stability --- essentially, you go into a stream and create these mini-floodplains that allow water to “spill out” of the main channel during high flows and onto these benches (or 2nd stage), slowing water velocity.
These benches also provide some ADDED benefits --- they are vegetated, slowing the velocity even more and allowing sediments in the water to settle out onto these floodplains.
The benches ALSO increase the bioreactive surface area of these streams (the goal is to triple the area); so the benches increase retention time (contact time) AND surface area for biology to work on removing nutrients from the water column, ultimately REDUCING the amount that is exported downstream.
A lot of work had proven that the two-stage increases channel stability, but the next question was: Does the two-stage improve water quality?
We first measured changes in turbidity (or water cloudiness)
This figure shows average turbidity (so higher = cloudier or less clear) in channelized (gray bars) compared to two-stage (red bars) systems
What we see is that turbidity is lower in the two-stage demonstrating an increase in water clarity
The mechanism behind this is that the two-stage slows water velocities allowing sediment (and associated phosphorus) settle out onto the floodplains
We also measured changes in N removal (via denitrification or the microbial process that converts NO3 to N2 gas, permanently removing N from the system)
This figure shows total N removal in the two-stage floodplains (in green) and we found that N removal was 3-24x higher in the two-stage floodplains compared to the stream
The mechanism here is that the floodplains are increasing the AREA for biological activity, including denitrification
These early studies really demonstrated that the two-stage is a viable tool in the nutrient management toolbox
But these studies also revealed some “best practices” that can maximize two-stage efficacy, including regular inundation, tile flow retention, and vegetation
Earlier, I mentioned that we eventually “returned” to the two-stage to examine its function 10 years later
So as a reminder, the two-stage was constructed in the Fall of 2007
At the time, the upstream channelized ditch was established as the experimental “control” for comparison with the newly-constructed two-stage
So this is what the newly-constructed two-stage and upstream channelized ditch looked like in 2007Importantly, no maintenance or dredging has occurred in the upstream reach since 2007
And this is what the restored two-stage looks like after 10 years, while this is what the upstream naturalized reach looks like after 10 years
As you can see, the stream has started to form its own natural floodplains (via hydrologic processes/variability)
And what we really wanted to know was, after 10 years of natural floodplain formation, how does the two-stage compare to the upstream system?? Is the two-stage still “better” at N removal than a “naturalizing channelized ditch”??
So we measured N removal via denitrification again and found that denitrification was >30% higher in the two-stage floodplains compared to the naturalized/channelized ditch
When we look at what was driving these differences, we can see that DN increased with increasing OM in floodplains soils and that the difference between restored and naturalized floodplains was likely because OM was 22% higher in the restored reach
And this makes sense if we think about the process that establishes both of these floodplains:
This is what the two-stage looked like immediately following construction
But this is what the two-stage look like after just 1 growing seasons demonstrating that rapid establishment of vegetation on the benches promoted organic matter accumulation through growth/decomposition as well as slowing water velocity that promoted OM deposition from the aquatic environment
I was then interested in determining if DN varied among lateral zones within the floodplains
First, DN was higher in the restored reach in both active floodplain zones (NS, MD), again demonstrating enhanced function as a result of restoration
I also found that DN varied among lateral zones in ONLY the restored reach, with DN higher in NS compared to MD compared to UP
And this was largely driven by variation in floodplain soil QUALITY among the 3 lateral zones in the restored reach
In particular, the C:N ratio decreased with increasing %organic matter in the NS floodplain soils, likely indicating the effect of frequent inundation events that deposited high quality OM (low C:N ratio) on the floodplain
In summary, we found that two-stage construction essentially “jump-started” the natural floodplain formation process THUS jumpstarting function (in this case N removal) of the ecosystem
Total floodplain area was nearly 6x higher in the two-stage compared to the “naturalizing” channelized ditch
It would take >30 years for the channelized stream to accumulate equivalent floodplain area or essentially “catch up” to the two-stage
Not to mention the loss of drainage (and/or land) in the meantime
While both floodplains removed N, the two-stage restored function (or enhanced N removal) more rapidly
So while it may be a big investment up-front, benefits are big and little to no maintenance is needed over the long term
Now I’m briefly going to return to my project timeline:
And recall that the two-stage benefits include: improving bank stability (decreasing bank erosion), enhancing denitrification (increasing permanent N removal), and improved water quality (decreasing N and P concentrations)
However, this 600 m reach of two-stage at the base of SDW only reduced total N export by ~10%
Essentially, the sheer amount of nutrients entering from row-crop agricultural fields throughout the watershed overwhelmed the two-stage (not a silver bullet)
That’s when we started thinking about this as a “watershed-scale” problem and attempted to address the source or amount of nutrients entering from the landscape by planting cover crops
The practice that we chose to implement and examine was winter cover crops because they have the potential to reduce N by addressing two main issues bare soil during times of high nutrient loss
Cover crops are crops – like grasses – that literally cover the soil and have been historically used to reduce soil erosion, improve soil quality, control weeds, etc. and they represent this “hallmark” of sustainable agricultural systems
Planted just before or after harvest, establishing roots and vegetative land cover in the fall
Some cover crop species will then overwinter and grow back in the spring, providing vegetative land cover in the spring during those critical times of nutrient export
Indeed, field-scale studies conducted on experimental farms have found that cover crops reduce N loss from tile drains by 13-94% (from Delaware to Iowa)
However, the goal of our study was to plant cover crops at the watershed-scale (>50% of acres) and examine their influence on nutrient loss from tile drains and stream outlet of a watershed dominated by working lands or those used/operated by the producers themselves (NOT on controlled, experimental farms)
The first year of the study was our “pre-treatment” year, or PRE watershed-scale planting of cover crops when only 2 fields or ~320 acres were planted in cover crops
In the fall of 2013, cover crop acreage increased to a little over 1600 acres or 67% of croppable acres in the watershed
Maintained >60% of croppable acres in cover crops for the next two years
Our ultimate goal was to “saturate” the watershed with cover crops to isolate the effect of watershed-scale cover crops while measuring dissolved N land P loss
Annual ryegrass --- great! If planted early… Need to plant in September or Early October to ensure root establishment before the first frost kill. Once it’s established, it has an extensive root system that uses A LOT of water and N (perfect combination) and removes these from deep soil profiles (up to 60 inches deep --- amazing)
We first identified tile drain outlets along the entire 8 km of Shatto Ditch and began collecting water samples directly from the flow of water at 23 representative, tile drain outlets or about 2 per field
Each tile drain was categorized as either WITH COVER or WITHOUT COVER CROPS by visually identifying cover crop growing in the field, and confirming with the local Soil and Water Conservation District or the government agency that was helping us with this project
In the 2013 pre-treatment year (i.e., before watershed scale planting of cover crops), only 2 fields were planted in cover crops and therefore tile drains with cover crops <<< tile drains without cover crops; in 2014-2016, cover crop acreage was dramatically increased, leaving us with an unbalanced experimental design where now the number of tile drains with cover crops >>> tile drains without cover crops
Nevertheless, we had a record of tile drains from fields with and without cover crops during each year of the four year study
We then collected water samples every 14days from a representative subset of tile drains throughout the water (those that flowed regularly), as well as the watershed outlet
Each water sample was analyzed for NO3 and SRP (or DRP) concentration, and we measured instantaneous flow at each tile drain outlet as well
First, we examined the temporal patterns of NO3 concentration
So just to quickly orient you to these graphs… Average NO3 is on the y-axis and time is on the x-axis so each dot represents average NO3 on each sampling date for samples collected from the stream (in blue), tiles draining fields without cover crops (in brown), and tiles draining fields with cover crops (in green)
During the 2013 pre-treatment year, we found that NO3 concentration from tiles draining fields without cover crops was much higher than those with cover crops
NO3 from tiles draining CC fields was variable but generally lower in the following three years of cover crop planting, especially during 2016
Temporal patterns of SRP were slightly different than NO3
First, let’s note that the magnitude of SRP concentration is MUCH lower than NO3 A LOT less SRP is being transported compared to NO3
During the pre-treatment year, tile SRP was variable but generally lower than stream SRP
With cover crop planting, tile SRP appears less variable and remains considerably lower than stream SRP
Using the nutrient concentration at each tile drain and mulitplying by the discharge from each tile drain, I am able to calculate the nutrient loss from each drain.
1 TD removed that represents the “headwater” or top of the stream – drains ~300 acres
Still included in total watershed export because that estimate is made with data from the two-stage section of stream
So just to show you what that data looks like, this figure shows daily discharge in blue, stream NO3 concentration from grab samples in green, and stream SRP concentrations in orange from October 2007 to October 2012
The pre-treatment year of the study described here began in October 2012 and we continued collecting grab samples from the watershed outlet from October 2012-October 2013 when cover crop acreage in SDW was minimal (~12%)
In the late summer/fall of 2013, cover crops were then planted on >60% of croppable acres in SDW, marking the beginning of the POST cover crop period
All of this equals
However, because our goal was to examine changes in watershed export (again, mass loss), we needed to use these biweekly grab samples to estimate DAILY N and P loss from the watershed
In order to do this, we first estimated daily N and P concentration
For NO3, we used the linear relationship between NO3 concentration and discharge to PREDICT daily NO3 concentration using the discharge value from that given day
For SRP, we interpolated between grab samples
And we note that choosing these methods was informed by both our data and previously published research
We then multiplied daily concentration by daily discharge to estimate daily NO3 and SRP export from the watershed outlet over the entire period of record
So once we’ve estimated daily NO3 and SRP loss from the watershed, we aggregated or summed daily values to compare ANNUAL NO3 or SRP export before and after cover crop planting
These bars represent TOTAL annual NO3 loss during pre (in brown) and post (in green) cover crop years
And we found that average total annual NO3 export during the post cover crop period was 11% lower than the pre-cover crop period
Additionally, we found that total annual SRP export during the post cover crop period was 30% lower than the pre-cover crop period
However, as you can see, there is A LOT of year-to-year variability in the total nutrient export, which is largely driven by variation in FLOW changes in the timing and amount of precipitation that feed flows
Therefore, we felt that accounting for variability in flow was
So once we’ve estimated daily NO3 and SRP loss from the watershed, we aggregated or summed daily values to compare ANNUAL NO3 or SRP export before and after cover crop planting
These bars represent TOTAL annual NO3 loss during pre (in brown) and post (in green) cover crop years
And we found that average total annual NO3 export during the post cover crop period was 11% lower than the pre-cover crop period
Additionally, we found that total annual SRP export during the post cover crop period was 30% lower than the pre-cover crop period
However, as you can see, there is A LOT of year-to-year variability in the total nutrient export, which is largely driven by variation in FLOW changes in the timing and amount of precipitation that feed flows
Therefore, we felt that accounting for variability in flow was
Thus, we used flow duration curve analysis to account for variability in flow
A flow duration curve relates flow values to the percent of time those values have been met or exceeded; so the x-axis represents the duration amount, or “percent of time”, as in cumulative frequency distribution and the y-axis represents the flow value associated WITH that percent of time.
So when we plotted the flow duration curves for pre-CC years (2008-2013) and post-CC years (2014-2016) and found that the curves looked very similar.
We then used these flow duration curves to categorize NO3 export according to standard flow intervals
When we examined WHEN NO3 export was occurring, we found that >70% of export occurred during high flows and moist conditions
So our first question, was how does cover crop planting influence N and P loss from tile drains?
And we answered this by comparing median instantaneous nutrient load between tiles without cover crops (in the grayish brown) to tiles with cover crops (in the colored boxes) during each year of the study (2013, 2014, 2015, and 2016)
And from these direct comparisons, we found that NO3 load from tile drains with cover crops was 69-90% lower than tile drains without cover crops during spring
Similarly we found that SRP loads were 20-78% lower in tile drains from fields with cover crops in spring
While I am simply highlighting the results from spring – that really critical time of nutrient export from April to June – here, it is important to note that we COLLECTED water samples year-round and that these trends are generally consistent in fall, winter, and summer
Therefore, I concluded that N and P loss from fields with cover crops was lower than fields without cover crops, likely demonstrating the role that increasing vegetative land cover had in increasing nutrient retention in agricultural fields
We then wanted to determine which of the proximal drivers (concentration or discharge) were controlling nutrient loss from tile drains
Thus, we regressed instantaneous load with both concentration and discharge, determining that both N and P loss were controlled by discharge
Here again I am focusing specifically on data collected during the spring across all 4 years of the study and I found that tile drain NO3 loss increased with increasing discharge in tile drains regardless of cover crop planting
However!
Found that cover crops altered the relationship between tile flow and NO3- loss [in spring; LOWER intercept] such that NO3 loss from tiles with cover crops was 30% lower at similar flows
This finding indicated that cover crops were reducing the pool of leachable NO3 in the soil profiles during spring
In contrast, there was no difference in the regression relationship between tile flow and SRP load for tiles with and without cover crops Indicates that differences shown in the previous slide are largely driven by flow
These results emphasize that cover crops have the potentially to effectively manage nutrient loss, but managing flow may be a better way to reduce loss from fields (using drainage management)
Tile drain results reflected changes from individual fields, but we also wanted to know how watershed export, or the mass of N and P being transported by the stream itself differed between years with and without cover crops
In order to do this, we also collected and filtered triplicate, grab samples from the stream outlet on each sampling date (every 14 days), measuring both NO3 and SRP on the lachat flow injection auto-analyzer
Additionally, stream stage was measured continuously, which we related to hand measurements of in-stream discharge in order to estimate daily discharge from the SDW outlet
Importantly, because this work builds on previously published research from our lab, both water chemistry grab samples and stream discharge have been measured at the stream outlet (using similar methods) since 2007
Thus, we used flow duration curve analysis to account for variability in flow
A flow duration curve relates flow values to the percent of time those values have been met or exceeded; so the x-axis represents the duration amount, or “percent of time”, as in cumulative frequency distribution and the y-axis represents the flow value associated WITH that percent of time.
So when we plotted the flow duration curves for pre-CC years (2008-2013) and post-CC years (2014-2016) and found that the curves looked very similar.
We then used these flow duration curves to categorize NO3 export according to standard flow intervals
When we examined WHEN NO3 export was occurring, we found that >70% of export occurred during high flows and moist conditions